The following examples of using machine learning are provided by Wellers, et al. (2017), who stated that today's leading organizations
The following examples of using machine learning are provided by Wellers, et al. (2017), who stated that "today's leading organizations are using machine learning-based tools to automate decision processes. . . ."
1. Improving customer loyalty and retention. Companies mine customers' activities, transactions, and social interactions and sentiments to predict customer loyalty and retention. Companies can use machine learning, for example, to predict people's desire to change jobs and then employers can make attractive offers to keep the existing employees or to lure potential employees who work elsewhere to move to new employers.
2. Hiring the right people. Given an average of 250 applicants for a good job in certain companies, an AI-based program can analyze applicants' resumes and find qualified candidates who did not apply but placed their resume online.
3. Automating finance. Incomplete financial transactions that lack some data (e.g., order numbers) require special attention. Machine learning systems can learn how to detect and correct such situations, very quickly and at minimal cost. The AI program can take the necessary corrective action automatically.
4. Detecting fraud. Machine-learning algorithms use pattern recognition to detect fraud in real time. The program is looking for anomalies, and then it makes inferences regarding the type of detected activities to look for fraud.
Financial institutions are the major users of this program.
5. Providing predictive maintenance. Machine learning can find anomalies in the operation of equipment before it fails. Thus, corrective actions are done immediately at a fraction of a cost to repair equipment after it fails. In addition, optimal preventive maintenance can be done.
6. Providing retail shelf analysis. Machine learning combined with machine vision can analyze displays in physical stores to find whether items are in proper locations on the shelves, whether the shelves are properly stocked, and whether the product labels (including prices) are properly shown.
7. Making other predictions. Machine learning has been used for making many types of predictions ranging in areas from medicine to investments. An example is Google Flights, which predicts delays that have not been flagged yet by the airlines.
Questions for case
1. Discuss the benefits of combining machine learning with other AI technologies.
2. How can machine learning improve marketing?
3. Discuss the opportunities of improving human resource management.
4. Discuss the benefits for customer service.
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